A core theme of evolutionary game theory is to explore which strategies sustain cooperation in repeated social dilemmas [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]}. Given this extensive effort, it seems quite remarkable that very little is known about how such strategies with desirable properties can be learned most effectively. Instead, existing work tends to take the way in which people learn as given. While details vary between studies [15]}, [16]}, [17]}, [18]}, [19]}, [20]}, [21]}, [22]}, most often it is assumed that individuals adopt strategies that enhance their payoff, and that they abandon strategies that are personally disadvantageous. This modeling assumption could be justified on theoretical grounds if selfish payoff maximization were indeed an optimal learning policy. Here, we have thus explored under which conditions selfish learning can be expected to succeed. We show that selfish learning performs well when individuals wish to optimize their short run performance. However, if individuals are motivated by how well they fare eventually, selfish learning can be detrimental, even in populations that otherwise consist entirely of selfish learners.
